Analyzing Covariate Influence on Gender and Race Prediction from Near-Infrared Ocular Images

TL;DR

This paper investigates the potential to predict gender and race from near-infrared ocular images using simple texture descriptors, revealing high accuracy and analyzing various influencing factors.

Contribution

It demonstrates that gender and race can be accurately predicted from iris images using texture features, and provides a comprehensive analysis of influencing factors and generalizability.

Findings

Gender prediction accuracy: 86%

Race prediction accuracy: 90%

Analysis of factors affecting prediction performance

Abstract

Recent research has explored the possibility of automatically deducing information such as gender, age and race of an individual from their biometric data. While the face modality has been extensively studied in this regard, the iris modality less so. In this paper, we first review the medical literature to establish a biological basis for extracting gender and race cues from the iris. Then, we demonstrate that it is possible to use simple texture descriptors, like BSIF (Binarized Statistical Image Feature) and LBP (Local Binary Patterns), to extract gender and race attributes from an NIR ocular image used in a typical iris recognition system. The proposed method predicts gender and race from a single eye image with an accuracy of 86% and 90%, respectively. In addition, the following analysis are conducted: (a) the role of different parts of the ocular region on attribute prediction; (b) the influence of gender on race prediction, and vice-versa; (c) the impact of eye color on gender and race prediction; (d) the impact of image blur on gender and race prediction; (e) the generalizability of the method across different datasets; and (f) the consistency of prediction performance across the left and right eyes.